A Pharmacogenetic Study of the NCIC CTG Clinical Trial My.10: Single Nucleotide Polymorphisms, Prognosis, and Predicting Benefit from IMiD® Compound Maintenance Therapy Following Autologous Stem Cell Transplant for Multiple Myeloma
Bibliographic record
Abstract
Abstract Introduction: In multiple myeloma, randomized trials of maintenance therapy using thalidomide or lenalidomide following autologous transplant (ASCT) demonstrated prolonged progression-free survival (PFS), but overall survival benefit was inconsistent. The MY.10 trial (n=332) demonstrated improved PFS with thalidomide-prednisone maintenance therapy, but quality of life was inferior and overall survival was not improved. As a randomized trial with a no-treatment control arm and biospecimen collection, MY.10 provides a unique opportunity to identify predictors of benefit from therapy that could be used to personalize treatment. Immunomodulatory drug action is mediated in part through binding to cereblon (CRBN) and downregulation of IRF4. Candidate single nucleotide polymorphisms (SNPs) were selected from the CRBN/IRF4 pathway and other genes previously associated with myeloma prognosis, drug response and toxicity from literature review. The aim of this study was to investigate associations between candidate SNPs and benefit from immunomodulatory compound-based maintenance therapy, as well as prognostic impact. Methods: Genomic DNA was extracted from samples preserved in Trizol¨. SNPs were genotyped using TaqMan¨ genotyping assays on the ViiA7 qPCR platform. Analysis was performed on 187 patients with available samples and clinical data (86 in treatment arm; 101 in observation arm). Cox regression models were performed using SAS clinical 2.0 for prognostic impact of a single SNP on PFS, assessed with and without adjusted covariates (age, stage, performance status and response to ASCT). In treatment benefit analysis, PFS was modeled with genotype, treatment assignment and their interaction term. Results: SNPs with significant prognostic impact for PFS in all 187 patients were found in CRBN (rs1672753) (HR 0.59, 95%CI 0.4-0.86; p=0.006) and the following SNPs which had been found to be prognostic in prior studies were validated: CYP1A1 (rs1048943) (HR 1.98, 95%CI 1.19-3.27; p=0.008), CYP1B1 (rs1056836) (HR 0.72, 95%CI 0.52-0.99; p=0.043), and trend toward significance for SNP in CYP1A2 (rs2069514) (HR 2.02, 95%CI 0.98-4.15; p=0.057). Of these, only the CYP1A1 SNP was found to be possibly predictive of greater benefit from maintenance therapy. A SNP in ABCA1 (rs363717), previously associated with thalidomide-induced neuropathy, was prognostic in this study (HR 1.44, 95%CI 1.03-2.02; p=0.033) but not predictive of treatment benefit. Table 1 lists SNPs associated with a differential level of benefit from maintenance therapy, including SNPs in FAM179a and ICAM1 that were possibly associated with harm from maintenance therapy. Table 1. SNPs and treatment interaction with significant impact on PFS. HR represent hazard ratio of treatment versus observation for a given genotype group. Asterisk Indicates trend toward significance. Wildtype Heterozygote & mutant p-value interaction Gene dbSNP ID SNP HR 95% CI HR 95% CI FAM179A rs1053667 T>C 0.46 0.33, 0.66 2.28 0.78, 6.65 0.003 ICAM1 rs1799969 G>A 0.51 0.36, 0.73 1.75 0.83, 3.70 0.007 CASP3 rs1049216 A>G 0.82 0.54, 1.25 0.41 0.25, 0.69 0.029 IRF4 rs12203592 C>T 0.69 0.47, 1.02 0.38 0.21, 0.69 0.062* CYP1A1 rs1048943 A>G 0.62 0.44, 0.87 0.11 0.02, 0.52 0.079* Conclusion: Previously identified SNPs in drug metabolism and drug efflux genes were validated to be prognostic in this cohort. The prognostic but not predictive significance of a SNP in CRBN suggests a possible role for CRBN in multiple myeloma disease biology independent of immunomodulatory therapy. SNPs predictive of differing degrees of benefit from immunomodulatory maintenance were found in genes involved in immunomodulatory activation, drug action and cell adhesion mediated drug resistance; SNPs in FAM179A and ICAM1 were possibly associated with harm from thalidomide maintenance and could potentially be used to identify patients unsuitable for immunomodulatory maintenance therapy. These results may be applicable to lenalidomide and pomalidomide, since immunomodulatory compounds exhibit similar chemical structures and mechanisms of action. Validation of these SNPs in prospective clinical trials involving other immunomodulatory can test this hypothesis. Further studies are needed to confirm our findings. Disclosures Off Label Use: thalidomide and prednisone maintenance for multiple myeloma. Bahlis:Celgene: Consultancy, Honoraria, Research Funding, Speakers Bureau; Amgen: Consultancy; Johnson & Johnson: Speakers Bureau; Johnson & Johnson: Consultancy; Johnson & Johnson: Research Funding. Song:Celgene Canada: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding. White:Celgene Canada: Honoraria, Research Funding. Chen:Celgene: Consultancy, Honoraria, Research Funding. Seftel:Celgene: Honoraria, Research Funding. Howsen-Jan:Celgene: Honoraria, Research Funding. Reece:Amgen: Honoraria; Bristol-Myers Squibb: Research Funding; Merck: Research Funding; Novartis: Honoraria, Research Funding; Onyx: Consultancy; Millennium Takeda: Research Funding; Lundbeck: Honoraria; Otsuka: Research Funding; Janssen-Cilag: Consultancy, Honoraria, Research Funding; Celgene: Consultancy, Honoraria, Research Funding. Shepherd:Celgene: Honoraria, Research Funding. Chen:Celgene: Honoraria, Research Funding. Djurfeldt:Celgene: Research Funding. Reiman:Soricimed Biopharma Inc: Consultancy, Other: Scientific Advisory Board for Soricimed.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".